About ConnectomeDB

ConnectomeDB, launched in 2015, is an ongoing project aimed at creating and continually improving a comprehensive and highly accurate database of interacting LR pairs and relevant data analysis tools. Its primary goal is to enhance the understanding of cell-cell communication in humans and other mammalian species, helping to advance biological and medical discoveries.

Developers: Perkins Systems Biology & Genomics Lab & YCU Bioinformatics Lab.

ConnectomeDB2025 - Database & Online Resource for Cell-to-Cell Communication Predictions

  • 3,436 Manually Curated Human LR pairs: comprehensive and most accurate with primary literature support
  • 1,081 Ligands & 857 Receptors: gene cards with extensive metadata and annotations
  • Biologically Relevant Content: ontologies, cancers and other diseases annotations
  • Homologous Interactions: ~98% LR pairs in mouse and other vertebrate species
  • Free, no-code, and user-friendly resource: developed with Quarto 1.6+ and BioRender

Publication: ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction (Journal Title, 2025)
GitHub repo: https://github.com/bioinfo-YCU/ConnectomeDB

Online implementation of ConnectomeDB2025

IMPORTANT: Read our Terms & Conditions before using this resource.

To access ConnectomeDB2025 online, an internet connection and a web browser are required. The following operating systems and major web browsers have been tested and are guaranteed to work:

  • Windows 10/11: Google Chrome, Safari, Microsoft Edge
  • macOS 15+: Google Chrome, Safari
  • Rocky Linux 8/9: Google Chrome, Mozilla Firefox
  • Debian: Google Chrome, Mozilla Firefox

Problems acessing/using this resource?
Please fill out the Inquiry form below or contact us via this email.

Not familiar with ConnectomeDB 2025 or want to learn more?
Please check out our tutorials.

Your Feedback

We welcome your feedback and suggestions for improving ConnectomeDB content and would be glad to hear any ideas for further development.
If you would like to contribute to the ConnectomeDB project, please visit the GitHub repository or email us.

NATMI (Network Analysis Toolkit for Multicellular Interactions)

  • our Python-based toolkit designed for constructing and analyzing multi-cellular communication networks in multiple species
  • predicts cell-to-cell communication at the levels of niches, tissues, and the entire organism
  • can use both single-cell and bulk gene expression and proteomic data

Publication: Predicting cell-to-cell communication networks using NATMI (Nat commun, 2020)
GitHub repo: https://github.com/forrest-lab/NATMI

Please cite the following papers if you use ConnectomeDB or NATMI in your publication:

  1. ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction, P Liu, et al. Journal Title XX (YY), ZZZ (2025)
    This work describes ConnectomeDB2025, which is our most current ligand-receptor (LR) pair database and online resource.

  2. Predicting cell-to-cell communication networks using NATMI, R Hou, et al. Nature communications 11 (1), 5011 (2020)
    This study introduces NATMI (Network Analysis Toolkit for Multicellular Interactions) and the ConnectomeDB2020 release.

  3. A draft network of ligand–receptor-mediated multicellular signalling in human, JA Ramilowski, et al. Nature communications 6 (1), 7866 (2015)
    This is the original resource upon which subsequent ConnectomeDB releases are based, and is widely used by many other LR databases.

Let us Help You

For questions related to the ConnectomeDB2025 online resource, please fill out the Inquiry form below or contact us via this email.

For general inquiries, please send Alistair, Jordan, and Rui an email.